Enhancing disciplinary diversity and inclusion in conservation science and practice based on a case study of the Society for Conservation Biology
Bibliographic record
Abstract
Effective conservation requires a variety of perspectives that center on different ways of knowing. Disciplinary diversity and inclusion (DDI) offers an important means of integrating different ways of knowing into pressing conservation challenges. However, DDI means more than multiple disciplinary approaches to conservation; cognitive diversity and epistemic justice are key. In 2020, the Disciplinary Inclusion Task Force was formed via a grassroots movement of the Society for Conservation Biology (SCB) to assess the extent of DDI and to chart a path to increase DDI. First, we assessed past and present SCB governance documents. Next, we surveyed current SCB members (n = 577). Finally, we surveyed nonmember conservationists (n = 213). Members who were not biological scientists perceived SCB as less diverse (21.4% vs. 16%) and not equitable (21.8% vs. 161%), and, although the majority (44) of nonmembers reported that their work aligned reasonably well with the mission of the SCB, they thought the organization focused on biological sciences. Despite SCB's mission to be diverse and inclusive, realizing this mission will likely require diverse epistemological perspectives and shifting from top-down models of knowledge transfer. In centering on DDI, SCB can achieve its aspirations of connecting members across disciplines and ways of knowing to foster diverse perspectives and practices. We recommend that SCB and other organizations develop mechanisms to increase recruitment and retention of diverse members and leadership as well as expand strategic partnerships to flatten disciplinary hierarchies and promote inclusivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.029 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".